PT Journal
AU ATLAS Collaboration (Aad, Gea
   Alvarez Piqueras, D
   Cabrera Urban, S
   Castillo Gimenez, V
   Costa, MJ
   Fernandez Martinez, P
   Ferrer, A
   Fiorini, L
   Fuster, J
   Garcia, C
   Garcia Navarro, JE
   Gonzalez de la Hoz, S
   Hernandez Jimenez, Y
   Higon-Rodriguez, E
   Irles Quiles, A
   Jimenez Pena, J
   Kaci, M
   King, M
   Lacasta, C
   Lacuesta, VR
   Marti-Garcia, S
   Mitsou, VA
   Oliver Garcia, E
   Pedraza Lopez, S
   Perez Garcia-Estañ, MT
   Romero Adam, E
   Ros, E
   Salt, J
   Sanchez Martinez, V
   Soldevila, U
   Sanchez, J
   Valero, A
   Valladolid Gallego, E
   Valls Ferrer, JA
   Vos, M
TI A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector
SO European Physical Journal C
JI Eur. Phys. J. C
PY 2016
BP 238
EP 33pp
VL 76
IS 5
DI 10.1140/epjc/s10052-016-4065-1
LA English
AB The distribution of particles inside hadronic jets produced in the decay of boosted W and Z bosons can be used to discriminate such jets from the continuum background. Given that a jet has been identified as likely resulting from the hadronic decay of a boosted W or Z boson, this paper presents a technique for further differentiating Z bosons from W bosons. The variables used are jet mass, jet charge, and a b-tagging discriminant. A likelihood tagger is constructed from these variables and tested in the simulation of W' -> WZ for bosons in the transverse momentum range 200 GeV < p(T) < 400 GeV in root s = 8 TeV pp collisions with the ATLAS detector at the LHC. For Z-boson tagging efficiencies of epsilon(Z) = 90, 50, and 10 %, one can achieve W+-boson tagging rejection factors (1/epsilon(W+)) of 1.7, 8.3 and 1000, respectively. It is not possible to measure these efficiencies in the data due to the lack of a pure sample of high p(T), hadronically decaying Z bosons. However, the modelling of the tagger inputs for boosted W bosons is studied in data using a t (t) over bar -enriched sample of events in 20.3 fb(-1) of data at root s = 8 TeV. The inputs are well modelled within uncertainties, which builds confidence in the expected tagger performance.
ER